Formação de professores: uma lacuna na educação inclusiva: um estudo da formação do professor de língua portuguesa no Distrito Federal

A educação inclusiva se constitui hoje como realidade inegável, que necessita de discussões referentes ao ensino de alunos com deficiências físicas, sensoriais e cognitivas, por representarem um paradigma educacional novo e desconhecido pela maioria dos profissionais educadores. Esta monografia apresenta essa preocupação, considerando como foco de análise a formação do professor de Língua Portuguesa para o ensino básico na perspectiva da inclusão, com o intuito de investigar se há adequação na formação docente para o ensino inclusivo. Além da fundamentação teórica, foram utilizados como instrumentos de coleta de dados a análise das Diretrizes Curriculares Nacionais para o curso de Letras, um estudo dos currículos de quatro instituições de ensino superior do Distrito Federal e a entrevista semi-estruturada, realizada com cinco professoras de três escolas da rede pública do Distrito Federal, regentes de salas de aula inclusivas. O conjunto de ações dessa pesquisa tem a pretensão de verificar em que medida a formação obtida na graduação em Letras contribui para o trabalho pedagógico nas escolas, na perspectiva da inclusão do Distrito Federal

On the Generalizability of Experimental Results

The age-old question of the generalizability of the results of experiments that are conducted in artificial laboratory settings to more realistic inferential and decision making situations is considered in this paper. Conservatism in probability revision provides an example of a result that 1) has received wide attention, including attention in terms of implications for real-world decision making, on the basis of experiments conducted in artificial settings and 2) is now apparently thought by many to be highly situational and not at all a ubiquitous phenomenon, in which case its implications for real-world decision making are not as extensive as originally claimed. In this paper we consider the questions of generalizations from the laboratory to the real world in some detail, both within the context of the experiments regarding conservatism and within a more general context. In addition, we discuss some of the difficulties inherent in experimentation in realistic settings, suggest possible procedures for avoiding or at least alleviating such difficulties, and make a plea for more realistic experiments

Noncollinear paramagnetism of a GaAs two-dimensional hole system.

We have performed transport measurements in tilted magnetic fields in a two-dimensional hole system grown on the surface of a (311)A GaAs crystal. A striking asymmetry of Shubnikov-de Haas oscillations occurs upon reversing the in-plane component of the magnetic field along the low-symmetry [2[over ¯]33] axis. As usual, the magnetoconductance oscillations are symmetric with respect to reversal of the in-plane field component aligned with the high-symmetry [011[over ¯]] axis. Our observations demonstrate that an in-plane magnetic field can generate an out-of-plane component of magnetization in a low-symmetry hole system, creating new possibilities for spin manipulation.This work was supported by the Australian Research Council (ARC) under the DP scheme and by the NSF under Grant No. DMR-1310199. ARH acknowledges an ARC DOR award.This is the accepted manuscript. The final version is available from APS at http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.113.236401

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using Complementary DFT and Machine Learning Approaches

<p>There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work however, even the most efficient electronic structure methods such as density functional theory, are too time consuming to explore more than a tiny fraction of all possible hybrid 2D materials. Here we demonstrate that a combination of DFT and machine learning techniques provide a practical method for exploring this parameter space much more efficiently than by DFT or experiment. As a proof of concept we applied this methodology to predict the interlayer distance and band gap of bilayer heterostructures. Our methods quickly and accurately predicted these important properties for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.</p

Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery

The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and accurate tool to predict the properties of novel materials, it still demands extensive and expensive state-of-the-art computational resources. Additionally, they can be often extremely time consuming. We describe a time and resource-efficient machine learning approach to create a large dataset of structural properties of van der Waals layered structures. In particular, we focus on the interlayer energy and the elastic constant of layered materials composed of two different 2-dimensional (2D) structures, that are important for novel solid lubricant and super-lubricant materials. We show that machine learning models can recapitulate results of computationally expansive approaches (i.e. density functional theory) with high accuracy

Non-Abelian statistics and topological quantum information processing in 1D wire networks

Topological quantum computation provides an elegant way around decoherence, as one encodes quantum information in a non-local fashion that the environment finds difficult to corrupt. Here we establish that one of the key operations---braiding of non-Abelian anyons---can be implemented in one-dimensional semiconductor wire networks. Previous work [Lutchyn et al., arXiv:1002.4033 and Oreg et al., arXiv:1003.1145] provided a recipe for driving semiconducting wires into a topological phase supporting long-sought particles known as Majorana fermions that can store topologically protected quantum information. Majorana fermions in this setting can be transported, created, and fused by applying locally tunable gates to the wire. More importantly, we show that networks of such wires allow braiding of Majorana fermions and that they exhibit non-Abelian statistics like vortices in a p+ip superconductor. We propose experimental setups that enable the Majorana fusion rules to be probed, along with networks that allow for efficient exchange of arbitrary numbers of Majorana fermions. This work paves a new path forward in topological quantum computation that benefits from physical transparency and experimental realism.Comment: 6 pages + 17 pages of Supp. Mat.; 10 figures. Supp. Mat. has doubled in size to establish results more rigorously; many other improvements as wel

High (but Not Low) Urinary Iodine Excretion Is Predicted by Iodine Excretion Levels from Five Years Ago

Background: It has not been investigated whether there are associations between urinary iodine (UI) excretion measurements some years apart, nor whether such an association remains after adjustment for nutritional habits. The aim of the present study was to investigate the relation between iodine-creatinine ratio (ICR) at two measuring points 5 years apart. Methods: Data from 2,659 individuals from the Study of Health in Pomerania were analyzed. Analysis of covariance and Poisson regressions were used to associate baseline with follow-up ICR. Results: Baseline ICR was associated with follow-up ICR. Particularly, baseline ICR >300 mu g/g was related to an ICR >300 mu g/g at follow-up (relative risk, RR: 2.20; p < 0.001). The association was stronger in males (RR: 2.64; p < 0.001) than in females (RR: 1.64; p = 0.007). In contrast, baseline ICR <100 mu g/g was only associated with an ICR <100 mu g/g at follow-up in males when considering unadjusted ICR. Conclusions: We detected only a weak correlation with respect to low ICR. Studies assessing iodine status in a population should take into account that an individual with a low UI excretion in one measurement is not necessarily permanently iodine deficient. On the other hand, current high ICR could have been predicted by high ICR 5 years ago. Copyright (C) 2011 S. Karger AG, Base

Spin-current modulation and square-wave transmission through periodically stubbed electron waveguides

Ballistic spin transport through waveguides, with symmetric or asymmetric double stubs attached to them periodically, is studied systematically in the presence of a weak spin-orbit coupling that makes the electrons precess. By an appropriate choice of the waveguide length and of the stub parameters injected spin-polarized electrons can be blocked completely and the transmission shows a periodic and nearly square-type behavior, with values 1 and 0, with wide gaps when only one mode is allowed to propagate in the waveguide. A similar behavior is possible for a certain range of the stub parameters even when two-modes can propagate in the waveguide and the conductance is doubled. Such a structure is a good candidate for establishing a realistic spin transistor. A further modulation of the spin current can be achieved by inserting defects in a finite-number stub superlattice. Finite-temperature effects on the spin conductance are also considered.Comment: 19 pages, 8 figure

High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications

© 2020 Wiley-VCH GmbH The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and accurate tool to predict the properties of novel materials, it still demands extensive and expensive state-of-the-art computational resources. Additionally, they can often be extremely time consuming. A time and resource efficient machine learning approach to create a dataset of structural properties of 18 million van der Waals layered structures is described. In particular, the authors focus on the interlayer energy and the elastic constant of layered materials composed of two different 2D structures that are important for novel solid lubricant and super-lubricant materials. It is shown that machine learning models can predict results of computationally expansive approaches (i.e., density functional theory) with high accuracy